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Author: foldfelis

docs/src/introduction.md

@@ -8,24 +8,24 @@ an operator mapping from infinite-dimensional space ``(u, t)`` to infinite-dimen
 Neural operator learns a continuous function between two continuous function spaces.
 The kernel can be trained on different geometry, including regular Euclidean space or a graph topology.
 
-## [Fourier Neural Operators](https://github.com/SciML/NeuralOperators.jl/blob/master/src/model.jl)
+## [Fourier Neural Operators](https://github.com/SciML/NeuralOperators.jl/blob/main/src/FNO/FNO.jl)
 
 Fourier neural operator (FNO) learns a neural operator with Dirichlet kernel to form a Fourier transformation.
 It performs Fourier transformation across infinite-dimensional function spaces and learns better than neural operator.
 
-## [Markov Neural Operators](https://github.com/SciML/NeuralOperators.jl/blob/master/src/model.jl)
+## [Markov Neural Operators](https://github.com/SciML/NeuralOperators.jl/blob/main/src/FNO/FNO.jl)
 
 Markov neural operator (MNO) learns a neural operator with Fourier operators.
 With only one time step information of learning, it can predict the following few steps with low loss
 by linking the operators into a Markov chain.
 
-## [Deep Operator Network](https://github.com/SciML/NeuralOperators.jl/blob/master/src/DeepONet.jl)
+## [Deep Operator Network](https://github.com/SciML/NeuralOperators.jl/blob/main/src/DeepONet/DeepONet.jl)
 
 Deep operator network (DeepONet) learns a neural operator with the help of two sub-neural network structures described as the branch and the trunk network.
 The branch network is fed the initial conditions data, whereas the trunk is fed with the locations where the target(output) is evaluated from the corresponding initial conditions.
 It is important that the output size of the branch and trunk subnets is same so that a dot product can be performed between them.
 
-## [Nonlinear Manifold Decoders for Operator Learning](https://github.com/SciML/NeuralOperators.jl/blob/master/src/NOMAD.jl)
+## [Nonlinear Manifold Decoders for Operator Learning](https://github.com/SciML/NeuralOperators.jl/blob/main/src/NOMAD/NOMAD.jl)
 
 Nonlinear Manifold Decoders for Operator Learning (NOMAD) learns a neural operator with a nonlinear decoder parameterized by a deep neural network which jointly takes output of approximator and the locations as parameters.
 The approximator network is fed with the initial conditions data. The output-of-approximator and the locations are then passed to a decoder neural network to get the target (output). It is important that the input size of the decoder subnet is sum of size of the output-of-approximator and number of locations.